“The research team proposes a target tracking algorithm based on three-dimensional arrival angle information and with asymptotic unbiased characteristics to address the challenge of long-distance non motorized single station target tracking. This innovative method is based on the relative motion model, constructing a motion and observation model suitable for non motorized single stations, and conducting in-depth analysis of the observability of the system. The research team proposed a constrained population least squares method with asymptotic unbiasedness to address the bias issue of the pseudo linear least squares algorithm, and proved its unbiasedness. The simulation experiment results show that in three-dimensional arrival angle tracking at the hundred kilometer level, the algorithm exhibits excellent performance under different angle measurement standard deviations and initial distance conditions. It has fast convergence speed, high positioning accuracy, and comparable operating efficiency to the pseudo linear least squares method. This research achievement provides a new and effective method for non motorized single station 3D arrival angle target tracking in long-distance scenarios, and has important practical application value. It not only improves the accuracy and stability of target tracking, but also provides new ideas for technological progress in related fields.”